Sentence Similarity
sentence-transformers
Safetensors
mpnet
feature-extraction
Generated from Trainer
dataset_size:281342
loss:CachedMultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use spl4shedEdu/mpnet_ISM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use spl4shedEdu/mpnet_ISM with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("spl4shedEdu/mpnet_ISM") sentences = [ "ez buy federal 556 nato xm855 62 grain green tip fmj us bulk 223 ammo for sale cheap bulk ammunition in fmj and jhp depotus american eagle 223556 fmj rebate 400 maximum per household case upcs accepted for mailin us identifiers is xm855lpc120rebate30 category of toolsandhomeimprovement", "ez buy federal 556 nato xm855 62 grain green tip fmj us bulk 223 ammo for sale cheap bulk ammunition in fmj and jhp depotus american eagle 223556 fmj rebate 400 maximum per household case upcs accepted for mailin us identifiers is xm855lpc120rebate30 category of toolsandhomeimprovement", "3m cable for dlink 10gbe cx4 module demcb300cx 3m module dlink list retailers identifiers is demcb300cx category of otherelectronics", "seat frame wiring harnessd 02082010 gb 2013 volkswagen golf china market electrics harness 4doorright gb identifiers is 1k4971369f category of automotive" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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